Traceability and provenance in big data medical systems

Richard McClatchey; Jetendr Shamdasani; Andrew Branson; Kamran Munir; Zsolt Kovacs; Giovanni Frisoni

doi:10.1109/CBMS.2015.10

Traceability and provenance in big data medical systems

Richard McClatchey, Jetendr Shamdasani, Andrew Branson, Kamran Munir, Zsolt Kovacs, Giovanni Frisoni

Centro San Giovanni di Dio - Fatebenefratelli

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Providing an appropriate level of accessibility to and tracking of data or process elements in large volumes of medical data, is an essential requirement in the Big Data era. Researchers require systems that provide traceability of information through provenance data capture and management to support their clinical analyses. We present an approach that has been adopted in the neuGRID and N4U projects, which aimed to provide detailed traceability to support research analysis processes in the study of biomarkers for Alzheimer's disease, but is generically applicable across medical systems. To facilitate the orchestration of complex, large-scale analyses in these projects we have adapted CRISTAL, a workflow and provenance tracking solution. The use of CRISTAL has provided a rich environment for neuroscientists to track and manage the evolution of data and workflow usage over time in neuGRID and N4U.

Original language	English
Title of host publication	Proceedings - IEEE Symposium on Computer-Based Medical Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	226-231
Number of pages	6
Volume	2015-July
ISBN (Print)	9781467367752
DOIs	https://doi.org/10.1109/CBMS.2015.10
Publication status	Published - Jul 24 2015
Event	28th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2015 - Sao Carlos and Ribeirao Preto, Brazil Duration: Jun 22 2015 → Jun 25 2015

Other

Other	28th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2015
Country/Territory	Brazil
City	Sao Carlos and Ribeirao Preto
Period	6/22/15 → 6/25/15

Keywords

Biomedical Analysis
Grid Computing
Neuroimaging
Provenance
Workflows

ASJC Scopus subject areas

Computer Science Applications
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/CBMS.2015.10

Cite this

Traceability and provenance in big data medical systems. / McClatchey, Richard; Shamdasani, Jetendr; Branson, Andrew; Munir, Kamran; Kovacs, Zsolt; Frisoni, Giovanni.

Proceedings - IEEE Symposium on Computer-Based Medical Systems. Vol. 2015-July Institute of Electrical and Electronics Engineers Inc., 2015. p. 226-231 7167491.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

McClatchey, R, Shamdasani, J, Branson, A, Munir, K, Kovacs, Z & Frisoni, G 2015, Traceability and provenance in big data medical systems. in Proceedings - IEEE Symposium on Computer-Based Medical Systems. vol. 2015-July, 7167491, Institute of Electrical and Electronics Engineers Inc., pp. 226-231, 28th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2015, Sao Carlos and Ribeirao Preto, Brazil, 6/22/15. https://doi.org/10.1109/CBMS.2015.10

@inproceedings{5e91e1fe869a4e059c9b0173b16d7641,

title = "Traceability and provenance in big data medical systems",

abstract = "Providing an appropriate level of accessibility to and tracking of data or process elements in large volumes of medical data, is an essential requirement in the Big Data era. Researchers require systems that provide traceability of information through provenance data capture and management to support their clinical analyses. We present an approach that has been adopted in the neuGRID and N4U projects, which aimed to provide detailed traceability to support research analysis processes in the study of biomarkers for Alzheimer's disease, but is generically applicable across medical systems. To facilitate the orchestration of complex, large-scale analyses in these projects we have adapted CRISTAL, a workflow and provenance tracking solution. The use of CRISTAL has provided a rich environment for neuroscientists to track and manage the evolution of data and workflow usage over time in neuGRID and N4U.",

keywords = "Biomedical Analysis, Grid Computing, Neuroimaging, Provenance, Workflows",

author = "Richard McClatchey and Jetendr Shamdasani and Andrew Branson and Kamran Munir and Zsolt Kovacs and Giovanni Frisoni",

year = "2015",

month = jul,

day = "24",

doi = "10.1109/CBMS.2015.10",

language = "English",

isbn = "9781467367752",

volume = "2015-July",

pages = "226--231",

booktitle = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "28th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2015 ; Conference date: 22-06-2015 Through 25-06-2015",

}

TY - GEN

T1 - Traceability and provenance in big data medical systems

AU - McClatchey, Richard

AU - Shamdasani, Jetendr

AU - Branson, Andrew

AU - Munir, Kamran

AU - Kovacs, Zsolt

AU - Frisoni, Giovanni

PY - 2015/7/24

Y1 - 2015/7/24

N2 - Providing an appropriate level of accessibility to and tracking of data or process elements in large volumes of medical data, is an essential requirement in the Big Data era. Researchers require systems that provide traceability of information through provenance data capture and management to support their clinical analyses. We present an approach that has been adopted in the neuGRID and N4U projects, which aimed to provide detailed traceability to support research analysis processes in the study of biomarkers for Alzheimer's disease, but is generically applicable across medical systems. To facilitate the orchestration of complex, large-scale analyses in these projects we have adapted CRISTAL, a workflow and provenance tracking solution. The use of CRISTAL has provided a rich environment for neuroscientists to track and manage the evolution of data and workflow usage over time in neuGRID and N4U.

AB - Providing an appropriate level of accessibility to and tracking of data or process elements in large volumes of medical data, is an essential requirement in the Big Data era. Researchers require systems that provide traceability of information through provenance data capture and management to support their clinical analyses. We present an approach that has been adopted in the neuGRID and N4U projects, which aimed to provide detailed traceability to support research analysis processes in the study of biomarkers for Alzheimer's disease, but is generically applicable across medical systems. To facilitate the orchestration of complex, large-scale analyses in these projects we have adapted CRISTAL, a workflow and provenance tracking solution. The use of CRISTAL has provided a rich environment for neuroscientists to track and manage the evolution of data and workflow usage over time in neuGRID and N4U.

KW - Biomedical Analysis

KW - Grid Computing

KW - Neuroimaging

KW - Provenance

KW - Workflows

UR - http://www.scopus.com/inward/record.url?scp=84944215942&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84944215942&partnerID=8YFLogxK

U2 - 10.1109/CBMS.2015.10

DO - 10.1109/CBMS.2015.10

M3 - Conference contribution

AN - SCOPUS:84944215942

SN - 9781467367752

VL - 2015-July

SP - 226

EP - 231

BT - Proceedings - IEEE Symposium on Computer-Based Medical Systems

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 28th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2015

Y2 - 22 June 2015 through 25 June 2015

ER -

Traceability and provenance in big data medical systems

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this